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Detecting rare structural variation in evolving microbial populations from new sequence junctions using breseq. | LitMetric

Detecting rare structural variation in evolving microbial populations from new sequence junctions using breseq.

Front Genet

Department of Molecular Biosciences, Center for Systems and Synthetic Biology, Center for Computational Biology and Bioinformatics, Institute for Cellular and Molecular Biology, The University of Texas at Austin Austin, TX, USA.

Published: February 2015

AI Article Synopsis

  • New mutations causing structural variations (SV) in genomes, like large deletions and gene duplications, are important for microbial evolution but are harder to predict than simple nucleotide changes.
  • An algorithm was developed within the breseq computational pipeline to detect these structural variants by analyzing split-read alignments from sequencing data, and it was tested using simulated E. coli data and actual samples from a long-term evolution experiment.
  • The findings revealed that SV contributed to about 25% of genetic diversity and highlighted the competition of mutations in key genes, while some mutations that seemed promising in early populations ultimately went extinct.

Article Abstract

New mutations leading to structural variation (SV) in genomes-in the form of mobile element insertions, large deletions, gene duplications, and other chromosomal rearrangements-can play a key role in microbial evolution. Yet, SV is considerably more difficult to predict from short-read genome resequencing data than single-nucleotide substitutions and indels (SN), so it is not yet routinely identified in studies that profile population-level genetic diversity over time in evolution experiments. We implemented an algorithm for detecting polymorphic SV as part of the breseq computational pipeline. This procedure examines split-read alignments, in which the two ends of a single sequencing read match disjoint locations in the reference genome, in order to detect structural variants and estimate their frequencies within a sample. We tested our algorithm using simulated Escherichia coli data and then applied it to 500- and 1000-generation population samples from the Lenski E. coli long-term evolution experiment (LTEE). Knowledge of genes that are targets of selection in the LTEE and mutations present in previously analyzed clonal isolates allowed us to evaluate the accuracy of our procedure. Overall, SV accounted for ~25% of the genetic diversity found in these samples. By profiling rare SV, we were able to identify many cases where alternative mutations in key genes transiently competed within a single population. We also found, unexpectedly, that mutations in two genes that rose to prominence at these early time points always went extinct in the long term. Because it is not limited by the base-calling error rate of the sequencing technology, our approach for identifying rare SV in whole-population samples may have a lower detection limit than similar predictions of SNs in these data sets. We anticipate that this functionality of breseq will be useful for providing a more complete picture of genome dynamics during evolution experiments with haploid microorganisms.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4301190PMC
http://dx.doi.org/10.3389/fgene.2014.00468DOI Listing

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